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################## Interpolation of Tmax Using Kriging #######################################
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########################### Kriging and Cokriging ###############################################
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#This script interpolates station values for the Oregon case study using Kriging and Cokring. #
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#The script uses LST monthly averages as input variables and loads the station data #
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#from a shape file with projection information. #
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#Note that this program: #
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#1)assumes that the shape file is in the current working. #
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#2)relevant variables were extracted from raster images before performing the regressions #
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# and stored shapefile #
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#This scripts predicts tmax using autokrige, gstat and LST derived from MOD11A1. #
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#also included and assessed using the RMSE,MAE,ME and R2 from validation dataset. #
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#TThe dates must be provided as a textfile. #
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#AUTHOR: Benoit Parmentier #
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#DATE: 07/15/2012 #
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#364-- #
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##################################################################################################
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###Loading R library and packages
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#library(gtools) # loading some useful tools
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library(mgcv) # GAM package by Wood 2006 (version 2012)
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library(sp) # Spatial pacakge with class definition by Bivand et al. 2008
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library(spdep) # Spatial pacakge with methods and spatial stat. by Bivand et al. 2012
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library(rgdal) # GDAL wrapper for R, spatial utilities (Keitt et al. 2012)
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library(gstat) # Kriging and co-kriging by Pebesma et al. 2004
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library(automap) # Automated Kriging based on gstat module by Hiemstra et al. 2008
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library(spgwr)
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library(gpclib)
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library(maptools)
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library(graphics)
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###Parameters and arguments
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infile1<- "ghcn_or_tmax_covariates_06262012_OR83M.shp" #GHCN shapefile containing variables for modeling 2010
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infile2<-"list_10_dates_04212012.txt" #List of 10 dates for the regression
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#infile2<-"list_365_dates_04212012.txt"
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infile3<-"LST_dates_var_names.txt" #LST dates name
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infile4<-"models_interpolation_05142012.txt" #Interpolation model names
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infile5<-"mean_day244_rescaled.rst"
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inlistf<-"list_files_05032012.txt" #Stack of images containing the Covariates
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path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations_07152012" #Jupiter LOCATION on Atlas for kriging
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#path<-"H:/Data/IPLANT_project/data_Oregon_stations" #Jupiter Location on XANDERS
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setwd(path)
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prop<-0.3 #Proportion of testing retained for validation
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seed_number<- 100 #Seed number for random sampling
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models<-7 #Number of kriging model
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out_prefix<-"_07132012_auto_krig_" #User defined output prefix
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###STEP 1 DATA PREPARATION AND PROCESSING#####
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###Reading the station data and setting up for models' comparison
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filename<-sub(".shp","",infile1) #Removing the extension from file.
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ghcn<-readOGR(".", filename) #reading shapefile
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CRS<-proj4string(ghcn) #Storing projection information (ellipsoid, datum,etc.)
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mean_LST<- readGDAL(infile5) #Reading the whole raster in memory. This provides a grid for kriging
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proj4string(mean_LST)<-CRS #Assigning coordinate information to prediction grid.
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##Extracting the variables values from the raster files
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lines<-read.table(paste(path,"/",inlistf,sep=""), sep=" ") #Column 1 contains the names of raster files
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inlistvar<-lines[,1]
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inlistvar<-paste(path,"/",as.character(inlistvar),sep="")
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covar_names<-as.character(lines[,2]) #Column two contains short names for covaraites
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s_raster<- stack(inlistvar) #Creating a stack of raster images from the list of variables.
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layerNames(s_raster)<-covar_names #Assigning names to the raster layers
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projection(s_raster)<-CRS
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#stat_val<- extract(s_raster, ghcn3) #Extracting values from the raster stack for every point location in coords data frame.
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pos<-match("ASPECT",layerNames(s_raster)) #Find column with name "value"
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r1<-raster(s_raster,layer=pos) #Select layer from stack
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pos<-match("slope",layerNames(s_raster)) #Find column with name "value"
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r2<-raster(s_raster,layer=pos) #Select layer from stack
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N<-cos(r1*pi/180)
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E<-sin(r1*pi/180)
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Nw<-sin(r2*pi/180)*cos(r1*pi/180) #Adding a variable to the dataframe
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Ew<-sin(r2*pi/180)*sin(r1*pi/180) #Adding variable to the dataframe.
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r<-stack(N,E,Nw,Ew)
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rnames<-c("Northness","Eastness","Northness_w","Eastness_w")
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layerNames(r)<-rnames
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s_raster<-addLayer(s_raster, r)
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s_sgdf<-as(s_raster,"SpatialGridDataFrame") #Conversion to spatial grid data frame
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### adding var
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ghcn = transform(ghcn,Northness = cos(ASPECT*pi/180)) #Adding a variable to the dataframe
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ghcn = transform(ghcn,Eastness = sin(ASPECT*pi/180)) #adding variable to the dataframe.
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ghcn = transform(ghcn,Northness_w = sin(slope*pi/180)*cos(ASPECT*pi/180)) #Adding a variable to the dataframe
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ghcn = transform(ghcn,Eastness_w = sin(slope*pi/180)*sin(ASPECT*pi/180)) #adding variable to the dataframe.
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#Remove NA for LC and CANHEIGHT
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ghcn$LC1[is.na(ghcn$LC1)]<-0
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ghcn$LC3[is.na(ghcn$LC3)]<-0
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ghcn$CANHEIGHT[is.na(ghcn$CANHEIGHT)]<-0
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set.seed(seed_number) #Using a seed number allow results based on random number to be compared...
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dates <-readLines(paste(path,"/",infile2, sep=""))
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LST_dates <-readLines(paste(path,"/",infile3, sep=""))
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#models <-readLines(paste(path,"/",infile4, sep=""))
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#models<-5
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#Model assessment: specific diagnostic/metrics for GAM
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results_AIC<- matrix(1,length(dates),models+3)
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results_GCV<- matrix(1,length(dates),models+3)
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#Model assessment: general diagnostic/metrics
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results_RMSE <- matrix(1,length(dates),models+3)
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results_MAE <- matrix(1,length(dates),models+3)
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results_ME <- matrix(1,length(dates),models+3)
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results_R2 <- matrix(1,length(dates),models+3) #Coef. of determination for the validation dataset
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results_RMSE_f<- matrix(1,length(dates),models+3)
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#Screening for bad values: value is tmax in this case
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#ghcn$value<-as.numeric(ghcn$value)
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ghcn_all<-ghcn
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ghcn_test<-subset(ghcn,ghcn$value>-150 & ghcn$value<400)
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ghcn_test2<-subset(ghcn_test,ghcn_test$ELEV_SRTM>0)
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ghcn<-ghcn_test2
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#coords<- ghcn[,c('x_OR83M','y_OR83M')]
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###CREATING SUBSETS BY INPUT DATES AND SAMPLING
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ghcn.subsets <-lapply(dates, function(d) subset(ghcn, ghcn$date==as.numeric(d))) #Producing a list of data frame, one data frame per date.
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for(i in 1:length(dates)){ # start of the for loop #1
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#i<-3 #Date 10 is used to test kriging
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#This allows to change only one name of the
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date<-strptime(dates[i], "%Y%m%d")
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month<-strftime(date, "%m")
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LST_month<-paste("mm_",month,sep="")
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#adding to SpatialGridDataFrame
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#t<-s_sgdf[,match(LST_month, names(s_sgdf))]
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#s_sgdf$LST<-s_sgdf[c(LST_month)]
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mod <-ghcn.subsets[[i]][,match(LST_month, names(ghcn.subsets[[i]]))]
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ghcn.subsets[[i]]$LST <-mod[[1]]
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n<-nrow(ghcn.subsets[[i]])
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ns<-n-round(n*prop) #Create a sample from the data frame with 70% of the rows
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nv<-n-ns #create a sample for validation with prop of the rows
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ind.training <- sample(nrow(ghcn.subsets[[i]]), size=ns, replace=FALSE) #This selects the index position for 70% of the rows taken randomly
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ind.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training) #This selects the index position for testing subset stations.
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data_s <- ghcn.subsets[[i]][ind.training, ]
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data_v <- ghcn.subsets[[i]][ind.testing, ]
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###BEFORE Kringing the data object must be transformed to SDF
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coords<- data_v[,c('x_OR83M','y_OR83M')]
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coordinates(data_v)<-coords
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proj4string(data_v)<-CRS #Need to assign coordinates...
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coords<- data_s[,c('x_OR83M','y_OR83M')]
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coordinates(data_s)<-coords
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proj4string(data_s)<-CRS #Need to assign coordinates..
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#This allows to change only one name of the data.frame
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pos<-match("value",names(data_s)) #Find column with name "value"
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names(data_s)[pos]<-c("tmax")
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data_s$tmax<-data_s$tmax/10 #TMax is the average max temp for months
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pos<-match("value",names(data_v)) #Find column with name "value"
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names(data_v)[pos]<-c("tmax")
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data_v$tmax<-data_v$tmax/10
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#dstjan=dst[dst$month==9,] #dst contains the monthly averages for tmax for every station over 2000-2010
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##############
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###STEP 2 KRIGING###
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#Kriging tmax
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# hscat(tmax~1,data_s,(0:9)*20000) # 9 lag classes with 20,000m width
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# v<-variogram(tmax~1, data_s) # This plots a sample varigram for date 10 fir the testing dataset
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# plot(v)
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# v.fit<-fit.variogram(v,vgm(2000,"Sph", 150000,1000)) #Model variogram: sill is 2000, spherical, range 15000 and nugget 1000
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# plot(v, v.fit) #Compare model and sample variogram via a graphical plot
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# tmax_krige<-krige(tmax~1, data_s,mean_LST, v.fit) #mean_LST provides the data grid/raster image for the kriging locations to be predicted.
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krmod1<-autoKrige(tmax~1, data_s,s_sgdf,data_s) #Use autoKrige instead of krige: with data_s for fitting on a grid
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krmod2<-autoKrige(tmax~x_OR83M+y_OR83M,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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krmod3<-autoKrige(tmax~x_OR83M+y_OR83M+ELEV_SRTM,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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krmod4<-autoKrige(tmax~x_OR83M+y_OR83M+DISTOC,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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krmod5<-autoKrige(tmax~x_OR83M+y_OR83M+ELEV_SRTM+DISTOC,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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krmod6<-autoKrige(tmax~x_OR83M+y_OR83M+Northness+Eastness,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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krmod7<-autoKrige(tmax~x_OR83M+y_OR83M+Northness+Eastness,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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#krmod8<-autoKrige(tmax~LST,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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#krmod9<-autoKrige(tmax~x_OR83M+y_OR83M+LST,input_data=data_s,new_data=s_sgdf,data_variogram=data_s)
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krig1<-krmod1$krige_output #Extracting Spatial Grid Data frame
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krig2<-krmod2$krige_output
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krig3<-krmod3$krige_outpu
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krig4<-krmod4$krige_output
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krig5<-krmod5$krige_output
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krig6<-krmod6$krige_output #Extracting Spatial Grid Data frame
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krig7<-krmod7$krige_output
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#krig8<-krmod8$krige_outpu
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#krig9<-krmod9$krige_output
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#tmax_krig1_s <- overlay(krige,data_s) #This overlays the kriged surface tmax and the location of weather stations
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#tmax_krig1_v <- overlay(krige,data_v)
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#
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# #Cokriging tmax
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# g<-gstat(NULL,"tmax", tmax~1, data_s) #This creates a gstat object "g" that acts as container for kriging specifications.
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# g<-gstat(g, "SRTM_elev",ELEV_SRTM~1,data_s) #Adding variables to gstat object g
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# g<-gstat(g, "LST", LST~1,data_s)
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# vm_g<-variogram(g) #Visualizing multivariate sample variogram.
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# vm_g.fit<-fit.lmc(vm_g,g,vgm(2000,"Sph", 100000,1000)) #Fitting variogram for all variables at once.
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# plot(vm_g,vm_g.fit) #Visualizing variogram fit and sample
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# vm_g.fit$set <-list(nocheck=1) #Avoid checking and allow for different range in variogram
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# co_kriged_surf<-predict(vm_g.fit,mean_LST) #Prediction using co-kriging with grid location defined from input raster image.
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# #co_kriged_surf$tmax.pred #Results stored in SpatialGridDataFrame with tmax prediction accessible in dataframe.
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#spplot.vcov(co_kriged_surf) #Visualizing the covariance structure
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# tmax_cokrig1_s<- overlay(co_kriged_surf,data_s) #This overalys the cokriged surface tmax and the location of weather stations
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# tmax_cokrig1_v<- overlay(co_kriged_surf,data_v)
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for (j in 1:models){
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mod<-paste("krig",j,sep="")
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krmod<-get(mod)
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krig_val_s <- overlay(krmod,data_s) #This overlays the kriged surface tmax and the location of weather stations
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krig_val_v <- overlay(krmod,data_v) #This overlays the kriged surface tmax and the location of weather stations
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pred_krmod<-paste("pred_krmod",j,sep="")
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#Adding the results back into the original dataframes.
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data_s[[pred_krmod]]<-krig_val_s$var1.pred
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data_v[[pred_krmod]]<-krig_val_v$var1.pred
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#Model assessment: RMSE and then krig the residuals....!
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res_mod_kr_s<- data_s$tmax - data_s[[pred_krmod]] #Residuals from kriging training
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res_mod_kr_v<- data_v$tmax - data_v[[pred_krmod]] #Residuals from kriging validation
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RMSE_mod_kr_s <- sqrt(sum(res_mod_kr_s^2,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_s)))) #RMSE from kriged surface training
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RMSE_mod_kr_v <- sqrt(sum(res_mod_kr_v^2,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_v)))) #RMSE from kriged surface validation
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MAE_mod_kr_s<- sum(abs(res_mod_kr_s),na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_s))) #MAE from kriged surface training #MAE, Mean abs. Error FOR REGRESSION STEP 1: GAM
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MAE_mod_kr_v<- sum(abs(res_mod_kr_v),na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_v))) #MAE from kriged surface validation
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ME_mod_kr_s<- sum(res_mod_kr_s,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_s))) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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ME_mod_kr_v<- sum(res_mod_kr_v,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_v))) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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R2_mod_kr_s<- cor(data_s$tmax,data_s[[pred_krmod]],use="complete.obs")^2 #R2, coef. of determination FOR REGRESSION STEP 1: GAM
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R2_mod_kr_v<- cor(data_v$tmax,data_v[[pred_krmod]],use="complete.obs")^2 #R2, coef. of determinationFOR REGRESSION STEP 1: GAM
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#(nv-sum(is.na(res_mod2)))
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#Writing out results
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results_RMSE[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_RMSE[i,2]<- ns #number of stations used in the training stage
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results_RMSE[i,3]<- "RMSE"
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results_RMSE[i,j+3]<- RMSE_mod_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_MAE[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_MAE[i,2]<- ns #number of stations used in the training stage
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results_MAE[i,3]<- "MAE"
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results_MAE[i,j+3]<- MAE_mod_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_ME[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_ME[i,2]<- ns #number of stations used in the training stage
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results_ME[i,3]<- "ME"
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results_ME[i,j+3]<- ME_mod_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_R2[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_R2[i,2]<- ns #number of stations used in the training stage
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results_R2[i,3]<- "R2"
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results_R2[i,j+3]<- R2_mod_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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name3<-paste("res_kr_mod",j,sep="")
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#as.numeric(res_mod)
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#data_s[[name3]]<-res_mod_kr_s
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data_s[[name3]]<-as.numeric(res_mod_kr_s)
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#data_v[[name3]]<-res_mod_kr_v
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data_v[[name3]]<-as.numeric(res_mod_kr_v)
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#Writing residuals from kriging
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#Saving kriged surface in raster images
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data_name<-paste("mod",j,"_",dates[[i]],sep="")
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krig_raster_name<-paste("krmod_",data_name,out_prefix,".tif", sep="")
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writeGDAL(krmod,fname=krig_raster_name, driver="GTiff", type="Float32",options ="INTERLEAVE=PIXEL")
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krig_raster_name<-paste("krmod_",data_name,out_prefix,".rst", sep="")
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writeRaster(raster(krmod), filename=krig_raster_name) #Writing the data in a raster file format...(IDRISI)
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#krig_raster_name<-paste("Kriged_tmax_",data_name,out_prefix,".tif", sep="")
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#writeGDAL(tmax_krige,fname=krig_raster_name, driver="GTiff", type="Float32",options ="INTERLEAVE=PIXEL")
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#X11()
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#plot(raster(co_kriged_surf))
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#title(paste("Tmax cokriging for date ",dates[[i]],sep=""))
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#savePlot(paste("Cokriged_tmax",data_name,out_prefix,".png", sep=""), type="png")
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#dev.off()
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#X11()
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#plot(raster(tmax_krige))
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#title(paste("Tmax Kriging for date ",dates[[i]],sep=""))
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#savePlot(paste("Kriged_res_",data_name,out_prefix,".png", sep=""), type="png")
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#dev.off()
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#
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302
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}
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# #Co-kriging only on the validation sites for faster computing
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#
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# cokrig1_dv<-predict(vm_g.fit,data_v)
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# cokrig1_ds<-predict(vm_g.fit,data_s)
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# # data_s$tmax_cokr<-cokrig1_ds$tmax.pred
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# # data_v$tmax_cokr<-cokrig1_dv$tmax.pred
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#
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# #Calculate RMSE and then krig the residuals....!
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#
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# res_mod1<- data_v$tmax - data_v$tmax_kr #Residuals from kriging.
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# res_mod2<- data_v$tmax - data_v$tmax_cokr #Residuals from cokriging.
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#
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# RMSE_mod1 <- sqrt(sum(res_mod1^2,na.rm=TRUE)/(nv-sum(is.na(res_mod1)))) #RMSE from kriged surface.
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# RMSE_mod2 <- sqrt(sum(res_mod2^2,na.rm=TRUE)/(nv-sum(is.na(res_mod2)))) #RMSE from co-kriged surface.
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# #(nv-sum(is.na(res_mod2)))
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320
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321
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#Saving the subset in a dataframe
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data_name<-paste("ghcn_v_",dates[[i]],sep="")
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assign(data_name,data_v)
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data_name<-paste("ghcn_s_",dates[[i]],sep="")
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assign(data_name,data_s)
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326
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327
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# results[i,1]<- dates[i] #storing the interpolation dates in the first column
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328
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# results[i,2]<- ns #number of stations in training
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329
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# results[i,3]<- RMSE_mod1
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330
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# results[i,4]<- RMSE_mod2
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331
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#
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332
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# results_mod_n[i,1]<-dates[i]
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333
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# results_mod_n[i,2]<-(nv-sum(is.na(res_mod1)))
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334
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# results_mod_n[i,3]<-(nv-sum(is.na(res_mod2)))
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335
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}
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336
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337
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## Plotting and saving diagnostic measures
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338
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results_table_RMSE<-as.data.frame(results_RMSE)
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339
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results_table_MAE<-as.data.frame(results_MAE)
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340
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results_table_ME<-as.data.frame(results_ME)
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341
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results_table_R2<-as.data.frame(results_R2)
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342
|
|
343
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cname<-c("dates","ns","metric","krmod1", "krmod2","krmod3", "krmod4", "mkrod5")
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344
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colnames(results_table_RMSE)<-cname
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345
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colnames(results_table_MAE)<-cname
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346
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colnames(results_table_ME)<-cname
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347
|
colnames(results_table_R2)<-cname
|
348
|
|
349
|
|
350
|
#Summary of diagnostic measures are stored in a data frame
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351
|
tb_diagnostic1<-rbind(results_table_RMSE,results_table_MAE, results_table_ME, results_table_R2) #
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352
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#tb_diagnostic1_kr<-rbind(results_table_RMSE_kr,results_table_MAE_kr, results_table_ME_kr, results_table_R2_kr)
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353
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#tb_diagnostic2<-rbind(results_table_AIC,results_table_GCV, results_table_DEV,results_table_RMSE_f)
|
354
|
|
355
|
write.table(tb_diagnostic1, file= paste(path,"/","results_GAM_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")
|
356
|
#write.table(tb_diagnostic1_kr, file= paste(path,"/","results_GAM_Assessment_measure1_kr_",out_prefix,".txt",sep=""), sep=",")
|
357
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#write.table(tb_diagnostic2, file= paste(path,"/","results_GAM_Assessment_measure2_",out_prefix,".txt",sep=""), sep=",")
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358
|
|
359
|
|
360
|
#### END OF SCRIPT #####
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